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The purpose of this study is to examine whether the addition of biomechanical variables, such as variables associated with morphology, posture and balance, produce an increase in dissociation efficiency of 29 subjects with progressive scoliosis from 45 subjects with non progressive scoliosis. In a retrospective study, a learning group (Cobb: 27,1±10,6°) was used with five models comprising clinical, morphological, postural and balance variables and scoliosis progression. A testing group (Cobb: 14,2±8,3°) was then used to evaluate the models in a prospective study. In order to establish the efficiency of the addition of biomechanical variables, Lonstein and Carlson’s (1984) model was used as a reference.
The learning group was used to develop four classification models. The model without reduction was composed of 35 variables taken from the literature. In the model with reduction, an ANCOVA served as a reduction method to go from 35 to 8 variables and principal component analysis was used to go from 35 to 7 variables. The expert model was composed of eight variables selected according to clinical experience. Discriminant analysis, logistic regression and principal component analysis were applied in order to classify the subjects as progressive or non progressive. Logistic regression used with the model without reduction presented the highest efficiency (0,94), whereas discriminant analysis used with the expert model showed the lowest efficiency (0,87). These results show a direct relation between a group of clinical and biomechanical parameters and idiopathic scoliosis progression.
The testing group was used to apply the models developed from the learning group. The highest efficiency (0,89) was obtained with the use of discriminant analysis and logistic regression and the model without reduction, as the lowest (0,78) was obtained with the use of Lonstein and Carlson’s (1984) model. These values suggest that the addition of biomechanical variables to clinical data increases dissociation efficiency between progressive and non progressive scoliotic subjects.
In order to verify the precision of the models, the area under the ROC curve was calculated. The largest area under the ROC curve (0,93) was obtained with the discriminant analysis used with the model without reduction, whereas the lowest (0,63) was obtained with Lonstein and Carlson’s (1984) model. Lonstein and Carlson’s (1984) model could not separate the positive cases from the negative cases with the same amount of precision compared with the biomechanical models.
The addition of biomechanical variables to clinical data allowed increasing dissociation efficiency between progressive and non progressive scoliotic subjects. These results suggest that factors other than clinical parameters can identify patients at risk of progression. An approach based on many types of parameters takes into account the multi-factorial nature of idiopathic scoliosis and appears to be better adapted to predict it’s progression.